MLESAC Based Localization of Needle Insertion Using 2D Ultrasound Images

Abstract

In the 2D ultrasound image of ultrasound-guided percutaneous needle insertions, it is difficult to determine the positions of needle axis and tip because of the existence of artifacts and other noises. In this work the speckle is regarded as the noise of an ultrasound image, and a novel algorithm is presented to detect the needle in a 2D ultrasound image. Firstly, the wavelet soft thresholding technique based on BayesShrink rule is used to denoise the speckle of ultrasound image. Secondly, we add Otsu’s thresholding method and morphologic operations to pre-process the ultrasound image. Finally, the localization of the needle is identified and positioned in the 2D ultrasound image based on the maximum likelihood estimation sample consensus (MLESAC) algorithm. The experimental results show that it is valid for estimating the position of needle axis and tip in the ultrasound images with the proposed algorithm. The research work is hopeful to be used in the path planning and robot-assisted needle insertion procedures.